• Information Systems Review
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• v.17 no.2
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• pp.77-100
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• 2015

This study examined the factors that influence the smartphone user's decision to accept the personalized mobile advertisement. As a theoretical basis, we applied the privacy calculus model (PCM) that illustrates how consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. In particular, we investigated how smartphone users make a risk-benefit assessment under which personalized service as benefit-side factor and information privacy risks as a risk-side factor accompanying their acceptance of advertisements. Further, we extend the current PCM by considering advertisement fatigue as a new factor that may influence the user's acceptance. The research model with five (5) hypotheses was tested using data gathered from 215 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a mobile advertisement service was provided. The results showed that three (3) out of five (5) hypotheses were supported. First, we found that the intention to accept advertisements is positively and significantly influenced by the perceived value of personalization. Second, perceived advertisement fatigue was also found to be a strong predictor of the intention to accept advertisements. However, we did not find any evidence of direct influence of privacy risks. Finally, we found that the significant moderating effect between the perceived value of personalization and advertisement fatigue. This suggests that the firms should provide effective tailored advertisement that can increase the perceived value of personalization to mitigate the negative impacts of advertisement fatigue.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.254-266
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• 2020

The purpose of this study is to analyze the influence of the type of recommender system on consumer's perceived benefit and privacy risk. The result showed that the perceived usefulness and intension to click was high in the order of Hybrid-filtering, Bestseller, and SNS-based system. Privacy concern was high in order of SNS-based system, Hybrid-filtering, and Bestseller. Moderating effects of perceived personalization on the type of recommender system and perceived usefulness were significant. Finally perceived usefulness had positive effect, and privacy concern had negative effect on consumer's intension to click. This study has significant implications for digital marketing bt comparing consumer responses according to the type of recommended service. The result of this study can be helpful for providing and developing future recommender service.

• Informatization Policy
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• v.17 no.2
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• pp.68-85
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• 2010

This study extends the privacy calculus model to investigate the role of information transparency in influencing individual decision making on information disclosure. The proposed model integrates perceived usefulness and ease of use as benefit-side factors and information privacy risk as a risk-side factor accompanying information disclosure, and theorizes the effects of information transparency on the factors. The research model was tested using data gathered from 163 respondents through an online survey method. Results suggest that users'perception of information transparency not only increases the perceived benefits from the online site but also mitigates the risk related with information disclosure, resulting in higher intention to provide personal information to the site. Further, we find that online firms may improve users' perception of information transparency by providing explanation on why particular personal information is required and how it will be used.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.7
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• pp.746-757
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• 2003

The purpose of this study was to investigate the internet perceived risk segments in regard to clothing benefits sought, internet shopping attitude, and internet purchase intention. The subjects used for the study were 210 male and 338 female college students. The internet perceived risk consisted of size/defect risk, social psychological risk, privacy risk, delivery risk, and price risk. The clothing benefits sought had impression improvement, fashion, individuality, figure flaws compensation, and comfort factors. The results showed that consumers were segmented by four groups based on internet perceived risk factors : 1) privacy risk group, 2) size risk group. 3) low risk group, and 4) price/social psychological risk group. The four segmented groups differed in regard to clothing benefits sought, internet shopping attitude, and internet purchase intention. For example, in regard to clothing benefits sought, the price/social Psychological risk group sought fashion more than other groups. The low risk group considered figure flaws compensation benefit less important than other groups. Concerning internet shopping attitude, the low risk group had more favorable altitude toward trust, safety, diversity, exchange/return attributes of internet shopping than other groups. The privacy risk group had more favorable attitude toward convenience and price attributes of internet shopping. Regarding internet purchase intention, the low risk group had higher intention to purchase formal, casual, and sportswear. The size risk group had higher intention to purchase fashion accessories. Further group differences and implications of the results were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.1043-1063
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• 2019

With delegating proxy to process data before outsourcing, data owners in restricted access could enjoy flexible and powerful cloud storage service for productivity, but still confront with data integrity breach. Identity-based data auditing as a critical technology, could address this security concern efficiently and eliminate complicated owners' public key certificates management issue. Recently, Yu et al. proposed an Identity-Based Public Auditing for Dynamic Outsourced Data with Proxy Processing (https://doi.org/10.3837/tiis.2017.10.019). It aims to offer identity-based, privacy-preserving and batch auditing for multiple owners' data on different clouds, while allowing proxy processing. In this article, we first demonstrate this scheme is insecure in the sense that malicious cloud could pass integrity auditing without original data. Additionally, clouds and owners are able to recover proxy's private key and thus impersonate it to forge tags for any data. Secondly, we propose an improved scheme with provable security in the random oracle model, to achieve desirable secure identity based privacy-preserving batch public auditing with proxy processing. Thirdly, based on theoretical analysis and performance simulation, our scheme shows better efficiency over existing identity-based auditing scheme with proxy processing on single owner and single cloud effort, which will benefit secure big data storage if extrapolating in real application.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.105-114
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• 2015

This study aims 1) to identify distinctive consumer groups according to their perception of benefits and costs of Online Behavioral Advertising(OBA), 2) to explore differences among them, and 3) to investigate antecedent variables that affect the consumers' perception of OBA. Online survey data collected from 1,000 online users. The findings of this study are as follows. First, the result of cluster analysis identified four distinctive consumer groups according to the levels of perceived benefits and costs of OBA: 'Indifferent group', 'cost-centered group', 'benefit-centered group', and 'Benefit-cost balanced group'. Second, four consumer groups showed differences in their demographics, advertising related variables, privacy related variables, and technology related variables. Third, according to multinomial logistic analysis, it was found that there were different factors affecting consumers' perception of benefits and costs of OBA.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.235-242
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• 2022

One of the important research streams in the privacy literature for the past decade has been to discover factors affecting the decision-making process related to self-disclosure, called the cost-benefit analysis. However, although human behavior is greatly influenced by affective as well as cognitive factors, most of the factors found in previous studies are those with cognitive properties. Based on the awareness of this imbalanced situation, the study examines the role of affective factors on self-dislosure decision-making, especially SNS enjoyment and SNS fatigue. As a result of data analysis, the study finds that the influence of these affective factors is significant, and the influence of SNS enjoyment is greater than that of SNS fatigue. As for the relationship between the affective factors and the decision-making factors, the study finds that the positive affect(enjoyment) relates to only the positive evaluation factor(benefit) and the negative affect(fatigue) relates only the negative evaluation factor(cost), which demonstrate the congruent effect mechanism. Based on the result, the study discusses meaningful implications and suggestions for future studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.8
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• pp.1472-1491
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• 2011

As used product transactions are currently on the rise, the demand for transactions of secondhand digital content will grow in the future; thus, learning to make secure transactions while avoiding cyber attacks becomes an important issue. In this paper, we combine the new buyer's secret key, the new buyer's watermark to embed in resold digital content, and the reseller's encrypted watermark, which can prove legal ownership of the reseller. Using the privacy homomorphism property of RSA and exponential calculus, the original seller of digital content can verify the legality of the reseller and the new buyer. We also reduced the load of encryption/decryption digital content using a partial encryption/decryption algorithm to make our protocol more efficient and practical. In the proposed protocol, the seller is not able to conduct piracy and easily frame any other innocent secondhand buyer when a case of piracy is found. In fact, piracy can be clearly traced using the privacy homomorphism property of RSA and the embedded watermark mechanism. Further, in the proposed protocol, the seller himself can trace the piracy using exponential calculus. Since it is unnecessary to trust third party participation, the conspiracy problem is resolved and the new buyer is not required to participate in the dispute. Moreover, the seller, reseller and new buyer can simultaneously benefit from the secondhand transaction.

• Information Systems Review
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• v.25 no.1
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• pp.165-187
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• 2023

The continuous increase in life expectancy and high interest in health has brought about significant changes in the use of health information by the public according to the development of information technology represented by the Internet and smartphones. As the medical market expands to the mobile health environment, many health-related apps have been created and distributed, but the acceptance rate is slow as it has become challenging to provide services due to various regulations. In this study, perceived value, perceived risk factors (psychological risk, risk of time-loss, legal risk), and perceived benefits (usefulness, interaction, autonomy) were derived and verified as factors that affect the acceptance resistance of personal health record apps based on the privacy calculation model. In addition, by analyzing the moderating effect of trust in the manufacturer, how the perceived risk and perceived benefit affect the perceived value was verified. A survey was conducted on Korean college students who recognized the personal health record apps but did not use them, and 127 samples were analyzed using structural equations. As a result of hypothesis verification, perceived value has a negative effect on acceptance resistance, perceived risk (risk of time-loss) has a negative effect on perceived value, and perceived benefits (usefulness, interaction, autonomy) were found to have a positive effect on perceived value. Trust in manufacturers has weakened the impact of perceived risks (legal risk) on perceived values. This study is expected to play an important role in maintaining a competitive advantage in the personal health record app market environment by identifying and proposing detailed criteria for reducing the acceptance resistance of personal health record apps.